Ruichen (Richard) Yang

Ruichen (Richard) Yang

Undergradate Researcher

Portfolio

3D Visual Feedback System for Neuroprosthetics Control

This project introduces the Reviewer, a novel 3D visual feedback system that enhances myoelectric prosthesis control by allowing users to visualize their EMG signals in real-time. This system clarifies the relationship between muscle activity and the machine learning algorithms behind prosthetic control. With intuitive feedback, it tackles challenges like learning curves and misclassifications, helping users produce clearer, more consistent signals for improved neuroprosthetic usability.

Processing and Evaluation Pipeline for Neuromodulation of Respiratory Muscles

Respiratory complications are a leading cause of death in patients with chronic spinal cord injury (SCI), significantly impacting their quality of life. Neuromodulation, a promising therapeutic approach, involves electrical stimulation to modulate nerve activity and enhance respiratory function. To support this effort, a comprehensive MATLAB pipeline was developed to automatically detect, analyze, and compare electromyography (EMG) properties in SCI patients undergoing neuromodulation therapy, contributing to the development of improved diagnostic and therapeutic strategies aimed at alleviating respiratory complications in this population.

Manufacturing of a Multi-Function Implantable Microcontroller: Recorder, Stimulator, and Wireless Charger

The Bionode is a compact, all-in-one implantable device combining a recorder, stimulator, and wireless charger, designed by Professor Pedro Irazoqui’s lab. Engineered for implantation, the device’s versatility and small size make it ideal for advanced studies in neuromuscular function. This project focuses on adapting and manufacturing the Bionode to record Electromyography (EMG) data from rats’ soleus muscles, enabling real-time data transmission to a PC for wavelet compression and detailed analysis of motor unit activity, supporting advancements in neuroengineering research.

Automated MRI Toolkit for Brain Structure Analysis

Introducing an Automated MRI Toolkit that synthesizes T2-weighted images from T1-weighted MRI scans, enabling precise brain structure analysis without requiring multiple imaging modalities. This toolkit simplifies research workflows and reduces patient scan time.